高分辨率统计降尺度数据集NEX- GDDP对中国极端温度指数模拟能力的评估
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摘要
利用1986—2005年中国地面气象台站观测的格点化逐日气温资料(CN05.1)评估了高分辨率统计降尺度数据集NASA Earth Exchange/Global Daily Downscaled Projections(NEX-GDDP)中21个全球气候模式对中国极端温度指数的模拟能力。在选用了日最低温度最大值(TNx)、日最高温度最大值(TXx)、暖夜指数(TN90p)和暖昼指数(TX90p)来研究极端温度事件的变化。结果显示:(1)除MRI-CGCM3模拟的日最高温度最大值外,其余模式对4个指数的模拟结果均表现出与观测一致的上升趋势,但模拟结果的平均值相对观测平均低0.26℃/(10 a)(日最低温度最大值)、0.19℃/(10 a)(日最高温度最大值)、2.21%/(10 a)(暖夜指数)、1.04%/(10 a)(暖昼指数)。(2)不同模式对各指数变化趋势空间分布特征的模拟存在较大差别,对日最低温度最大值、日最高温度最大值、暖夜指数和暖昼指数模拟能力最优模式分别为CCSM4、CESM1-BGC、MIROC-ESM-CHEM和bcc-csm1-1。模式模拟的日最低温度最大值和日最高温度最大值气候态平均值与观测值的相关系数在0.97以上。暖夜指数和暖昼指数模拟结果与观测值的标准差比值为0.34—1.58,均方根误差变化为1.6%—3.47%,对这两个指数模拟能力较优的模式分别为MIROC-ESM-CHEM(暖夜指数)和CESM1-BGC(暖昼指数)。(3)综合模式对4个指数在气候态平均值和变化趋势模拟能力的评估结果来看,CanESM2、CESM1-BGC和MIROC-ESM-CHEM显示了相对较高的模拟能力。因此,在利用GDDP-NEX研究未来极端温度事件时,建议将它们作为优选模式。
The gridded observational air temperature dataset(CN05.1) for the period 1986-2005 over China is used to evaluate daily extreme temperature indices simulated by 21 models that participate the NASA Earth Exchange/Global Daily Downscaled Projections(NEX-GDDP). Four extreme temperature indices, including the lowest daily temperature maximum(TNx), the highest daily temperature(TXx), the warm night frequency(TN90 p) and warm day frequency(TX90 p), are adopted to investigate the change of extreme temperature. The major conclusions are as follows.(1) Except for TXx from the MRI-CGCM3, the four indices from other models show an upward tendency, which is consistent with observations. However, the magnitudes of their linear trends are less than that from observations with the values of 0.26℃/decade(TNx), 0.19℃/decade(TXx), 2.21% decade(TN90 p), 1.04%/decade(TX90 p), respectively.(2) There are large differences in spatial patterns of those indices between models. For the simulation of all the four indices, CCSM4 performs the best, CESM1-BGC, MIROC-ESM-CHEM ranking next in order of performance. The spatial patterns of climatological extreme indices can be simulated perfectly with the correlation coefficients of observations with TNx and TXx from all models exceeding 0.97. The ratios of standard deviations between simulations and observations for TN90 p and TX90 p vary from 0.34 to 1.58, and the root mean square errors are within 1.6%-3.47%.(3) Synthetical evaluation of the four extreme indices in term of their climatological means and linear trends indicates that the performances of three models(i.e., CanESM2, CESM1-BGC and MIROC-ESM-CHEM) are relatively better. Therefore, it is suggested that results of the above three models in the NEX-GDDP can be used to investigate the extreme temperature change in the future.
The gridded observational air temperature dataset(CN05.1) for the period 1986-2005 over China is used to evaluate daily extreme temperature indices simulated by 21 models that participate the NASA Earth Exchange/Global Daily Downscaled Projections(NEX-GDDP). Four extreme temperature indices, including the lowest daily temperature maximum(TNx), the highest daily temperature(TXx), the warm night frequency(TN90 p) and warm day frequency(TX90 p), are adopted to investigate the change of extreme temperature. The major conclusions are as follows.(1) Except for TXx from the MRI-CGCM3, the four indices from other models show an upward tendency, which is consistent with observations. However, the magnitudes of their linear trends are less than that from observations with the values of 0.26℃/decade(TNx), 0.19℃/decade(TXx), 2.21% decade(TN90 p), 1.04%/decade(TX90 p), respectively.(2) There are large differences in spatial patterns of those indices between models. For the simulation of all the four indices, CCSM4 performs the best, CESM1-BGC, MIROC-ESM-CHEM ranking next in order of performance. The spatial patterns of climatological extreme indices can be simulated perfectly with the correlation coefficients of observations with TNx and TXx from all models exceeding 0.97. The ratios of standard deviations between simulations and observations for TN90 p and TX90 p vary from 0.34 to 1.58, and the root mean square errors are within 1.6%-3.47%.(3) Synthetical evaluation of the four extreme indices in term of their climatological means and linear trends indicates that the performances of three models(i.e., CanESM2, CESM1-BGC and MIROC-ESM-CHEM) are relatively better. Therefore, it is suggested that results of the above three models in the NEX-GDDP can be used to investigate the extreme temperature change in the future.
引文
《第三次气候变化国家评估报告》编写委员会.2015.第三次气候变化国家评估报告.2版.北京:科学出版社.Third National Committee on Climate Change Assessment Report.2015.National Assessment Report on Climate Change.2nd ed.Beijing:Science Press (in Chinese)
段青云,夏军,缪驰远等.2016.全球气候模式中气候变化预测预估的不确定性.自然杂志,38(3):182-188.Duan Q Y,Xia J,Miao C Y,et al.2016.The uncertainty in climate change projections by global climate models.Chinese J Nat,38(3):182-188 (in Chinese)
高谦,江志红,李肇新.2017.多模式动力降尺度对中国中东部地区极端气温指数的模拟评估.气象学报,75(6):917-933.Gao Q,Jiang Z H,Li Z X.2017.Simulation and evaluation of multi-model dynamical downscaling of temperature extreme indices over the middle and east China.Acta Meteor Sinica,75(6):917-933 (in Chinese)
胡浩林,任福民.2016.CMIP5模式集合对中国区域性低温事件的模拟与预估.气候变化研究进展,12(5):396-406.Hu H L,Ren F M.2016.Simulation and projection for China's regional low temperature events with CMIP5 multi-model ensembles.Climate Change Res,12(5):396-406 (in Chinese)
姜大膀,富元海.2012.2℃全球变暖背景下中国未来气候变化预估.大气科学,36(2):234-246.Jiang D B,Fu Y H.2012.Climate change over China with a 2℃ global warming.Chinese J Atmos Sci,36(2):234-246 (in Chinese)
蒋帅,江志红,李伟等.2017.CMIP5模式对中国极端气温及其变化趋势的模拟评估.气候变化研究进展,13(1):11-24.Jiang S,Jiang Z H,Li W,et al.2017.Evaluation of the extreme temperature and its trend in China simulated by CMIP5 models.Climate Change Res,13(1):11-24 (in Chinese)
孔祥慧,毕训强.2016.利用区域气候模式对我国南方百年气温和降水的动力降尺度模拟.气候与环境研究,21(6):711-724.Kong X H,Bi X Q.2016.Simulation of temperature and precipitation during the last 100 years over southern China by a regional climate model.Clim Environ Res,21(6):711-724 (in Chinese)
《气候变化国家评估报告》编写委员会.2007.气候变化国家评估报告.北京:科学出版社.National Committee on Climate Change Assessment Report.2007.National Assessment Report on Climate Change.Beijing:Science Press (in Chinese)
任国玉,徐铭志,初子莹等.2005.近54年中国地面气温变化.气候与环境研究,10(4):717-727.Ren G Y,Xu M Z,Chu Z Y,et al.2005.Changes of surface air temperature in China during 1951-2004.Clim Environ Res,10(4):717-727 (in Chinese)
任国玉,陈峪,邹旭恺等.2010a.综合极端气候指数的定义和趋势分析.气候与环境研究,15(4):354-364.Ren G Y,Chen Y,Zou X K,et al.2010a.Definition and trend analysis of an integrated extreme climatic index.Clim Environ Res,15(4):354-364 (in Chinese)
任国玉,封国林,严中伟.2010b.中国极端气候变化观测研究回顾与展望.气候与环境研究,15(4):337-353.Ren G Y,Feng G L,Yan Z W.2010b.Progresses in observation studies of climate extremes and changes in mainland China.Clim Environ Res,15(4):337-353 (in Chinese)
王安乾,苏布达,王艳君等.2017.全球升温1.5℃与2.0℃情景下中国极端低温事件变化与耕地暴露度研究.气象学报,75(3):415-428.Wang A Q,Su B D,Wang Y J,et al.2017.Variation of the extreme low-temperature events and farmland exposure under global warming of 1.5℃ and 2.0℃.Acta Meteor Sinica,75(3):415-428 (in Chinese)
王丹,王爱慧.2017.1901～2013年GPCC和CRU降水资料在中国大陆的适用性评估.气候与环境研究,22(4):446-462.Wang D,Wang A H.2017.Applicability assessment of GPCC and CRU precipitation products in China during 1901 to 2013.Clim Environ Res,22(4):446-462 (in Chinese)
吴佳,高学杰.2013.一套格点化的中国区域逐日观测资料及与其它资料的对比.地球物理学报,56(4):1102-1111.Wu J,Gao X J.2013.A gridded daily observation dataset over China region and comparison with the other datasets.Chinese J Geophys,56(4):1102-1111 (in Chinese)
杨绚,李栋梁,汤绪.2014.基于CMIP5多模式集合资料的中国气温和降水预估及概率分析.中国沙漠,34(3):795-804.Yang X,Li D L,Tang X.2014.Probability assessment of temperature and precipitation over China by CMIP5 multi-model ensemble.J Desert Res,34(3):795-804 (in Chinese)
张宁,孙照渤,曾刚.2008.1955—2005年中国极端气温的变化.南京气象学院学报,31(1):123-128.Zhang N,Sun Z B,Zeng G.2008.Change of extreme temperatures in China during 1955-2005.J Nanjing Inst Meteor,31(1):123-128 (in Chinese)
赵俊虎,王启光,支蓉等.2012.中国极端温度的群发性研究.气象学报,70(2):302-310.Zhao J H,Wang Q G,Zhi R,et al.2012.A study of the extreme temperature group-occurring events in China.Acta Meteor Sinica,2012,70(2):302-310 (in Chinese)
周莉,兰明才,蔡荣辉等.2018.21世纪前期长江中下游流域极端降水预估及不确定性分析.气象学报,76(1):47-61.Zhou L,Lan M C,Cai R H,et al.2018.Projection and uncertainties of extreme precipitation over the Yangtze River valley in the early 21st century.Acta Meteor Sinica,76(1):47-61 (in Chinese)
周雅清,任国玉.2010.中国大陆1956—2008年极端气温事件变化特征分析.气候与环境研究,15(4):405-417.Zhou Y Q,Ren G Y.2010.Variation characteristics of extreme temperature indices in mainland China during 1956-2008.Clim Environ Res,15(4):405-417 (in Chinese)
Bao Y,Wen X Y.2017.Projection of China's near- and long-term climate in a new high-resolution daily downscaled dataset NEX-GDDP.J Meteor Res,31(1):236-249
Cao L J,Zhao P,Yan Z W,et al.2013.Instrumental temperature series in eastern and central China back to the nineteenth century.J Geophys Res Atmos,118(15):8197-8207
Chen H P,Sun J Q.2015.Assessing model performance of climate extremes in China:An intercomparison between CMIP5 and CMIP3.Clim Change,129(1-2):197-211
Chen H P,Sun J Q,Li H X.2017.Future changes in precipitation extremes over China using the NEX-GDDP high-resolution daily downscaled data-set.Atmos Ocean Sci Lett,10(6):403-410
Easterling D R,Alexander L V,Mokssit A,et al.2003.CCI/CLIVAR workshop to develop priority climate indices.Bull Amer Meteor Soc,84(10):1403-1407
Gao X J,Shi Y,Zhang D F,et al.2012.Climate change in China in the 21st century as simulated by a high resolution regional climate model.Chinese Sci Bull,57(10):1188-1195
Guo D L,Wang H J.2016.Comparison of a very-fine-resolution GCM with RCM dynamical downscaling in simulating climate in China.Adv Atmos Sci,33(5):559-570
Hawkins E,Sutton R.2009.The potential to narrow uncertainty in regional climate predictions.Bull Amer Meteor Soc,90(8):1095-1108
IPCC.2007.Climate Change 2007:The Physical Science Basis.Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.Cambridge:Cambridge University Press,996pp
IPCC.2013.Climate Change 2013:The Physical Science Basis.Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.Cambridge:Cambridge University Press,1535pp
Jiang D B,Tian Z P,Lang X M.2016.Reliability of climate models for China through the IPCC third to fifth assessment reports.Int J Climatol,36(3):1114-1133
Li W,Jiang Z H,Xu J J,et al.2016.Extreme precipitation indices over China in CMIP5 models.PartⅡ:Probabilistic projection.J Climate,29(24):8989-9004
Santer B D,Taylor K E,Gleckler P J,et al.2009.Incorporating model quality information in climate change detection and attribution studies.Proc Natl Acad Sci USA,106(35):14778-14783
Taylor K E,Stouffer R J,Meehl G A.2012.An overview of CMIP5 and the experiment design.Bull Amer Meteor Soc,93(4):485-498
Tebaldi C,Hayhoe K,Arblaster J,et al.2006.Going to the extremes:An intercomparison of model-simulated historical and future changes in extreme events.Clim Change,79(3-4):185-211
Thrasher B,Xiong J,Wang W L,et al.2013.Downscaled climate projections suitable for resource management.Eos,Trans Amer Geophys Union,94(37):321-323
Wang A H,Fu J J.2013.Changes in daily climate extremes of observed temperature and precipitation in China.Atmos Oceanic Sci Lett,2013,6(5):312-319
Xu Y,Gao X J,Shen Y,et al.2009.A daily temperature dataset over China and its application in validating a RCM simulation.Adv Atmos Sci,26(4):763-772
段青云,夏军,缪驰远等.2016.全球气候模式中气候变化预测预估的不确定性.自然杂志,38(3):182-188.Duan Q Y,Xia J,Miao C Y,et al.2016.The uncertainty in climate change projections by global climate models.Chinese J Nat,38(3):182-188 (in Chinese)
高谦,江志红,李肇新.2017.多模式动力降尺度对中国中东部地区极端气温指数的模拟评估.气象学报,75(6):917-933.Gao Q,Jiang Z H,Li Z X.2017.Simulation and evaluation of multi-model dynamical downscaling of temperature extreme indices over the middle and east China.Acta Meteor Sinica,75(6):917-933 (in Chinese)
胡浩林,任福民.2016.CMIP5模式集合对中国区域性低温事件的模拟与预估.气候变化研究进展,12(5):396-406.Hu H L,Ren F M.2016.Simulation and projection for China's regional low temperature events with CMIP5 multi-model ensembles.Climate Change Res,12(5):396-406 (in Chinese)
姜大膀,富元海.2012.2℃全球变暖背景下中国未来气候变化预估.大气科学,36(2):234-246.Jiang D B,Fu Y H.2012.Climate change over China with a 2℃ global warming.Chinese J Atmos Sci,36(2):234-246 (in Chinese)
蒋帅,江志红,李伟等.2017.CMIP5模式对中国极端气温及其变化趋势的模拟评估.气候变化研究进展,13(1):11-24.Jiang S,Jiang Z H,Li W,et al.2017.Evaluation of the extreme temperature and its trend in China simulated by CMIP5 models.Climate Change Res,13(1):11-24 (in Chinese)
孔祥慧,毕训强.2016.利用区域气候模式对我国南方百年气温和降水的动力降尺度模拟.气候与环境研究,21(6):711-724.Kong X H,Bi X Q.2016.Simulation of temperature and precipitation during the last 100 years over southern China by a regional climate model.Clim Environ Res,21(6):711-724 (in Chinese)
《气候变化国家评估报告》编写委员会.2007.气候变化国家评估报告.北京:科学出版社.National Committee on Climate Change Assessment Report.2007.National Assessment Report on Climate Change.Beijing:Science Press (in Chinese)
任国玉,徐铭志,初子莹等.2005.近54年中国地面气温变化.气候与环境研究,10(4):717-727.Ren G Y,Xu M Z,Chu Z Y,et al.2005.Changes of surface air temperature in China during 1951-2004.Clim Environ Res,10(4):717-727 (in Chinese)
任国玉,陈峪,邹旭恺等.2010a.综合极端气候指数的定义和趋势分析.气候与环境研究,15(4):354-364.Ren G Y,Chen Y,Zou X K,et al.2010a.Definition and trend analysis of an integrated extreme climatic index.Clim Environ Res,15(4):354-364 (in Chinese)
任国玉,封国林,严中伟.2010b.中国极端气候变化观测研究回顾与展望.气候与环境研究,15(4):337-353.Ren G Y,Feng G L,Yan Z W.2010b.Progresses in observation studies of climate extremes and changes in mainland China.Clim Environ Res,15(4):337-353 (in Chinese)
王安乾,苏布达,王艳君等.2017.全球升温1.5℃与2.0℃情景下中国极端低温事件变化与耕地暴露度研究.气象学报,75(3):415-428.Wang A Q,Su B D,Wang Y J,et al.2017.Variation of the extreme low-temperature events and farmland exposure under global warming of 1.5℃ and 2.0℃.Acta Meteor Sinica,75(3):415-428 (in Chinese)
王丹,王爱慧.2017.1901～2013年GPCC和CRU降水资料在中国大陆的适用性评估.气候与环境研究,22(4):446-462.Wang D,Wang A H.2017.Applicability assessment of GPCC and CRU precipitation products in China during 1901 to 2013.Clim Environ Res,22(4):446-462 (in Chinese)
吴佳,高学杰.2013.一套格点化的中国区域逐日观测资料及与其它资料的对比.地球物理学报,56(4):1102-1111.Wu J,Gao X J.2013.A gridded daily observation dataset over China region and comparison with the other datasets.Chinese J Geophys,56(4):1102-1111 (in Chinese)
杨绚,李栋梁,汤绪.2014.基于CMIP5多模式集合资料的中国气温和降水预估及概率分析.中国沙漠,34(3):795-804.Yang X,Li D L,Tang X.2014.Probability assessment of temperature and precipitation over China by CMIP5 multi-model ensemble.J Desert Res,34(3):795-804 (in Chinese)
张宁,孙照渤,曾刚.2008.1955—2005年中国极端气温的变化.南京气象学院学报,31(1):123-128.Zhang N,Sun Z B,Zeng G.2008.Change of extreme temperatures in China during 1955-2005.J Nanjing Inst Meteor,31(1):123-128 (in Chinese)
赵俊虎,王启光,支蓉等.2012.中国极端温度的群发性研究.气象学报,70(2):302-310.Zhao J H,Wang Q G,Zhi R,et al.2012.A study of the extreme temperature group-occurring events in China.Acta Meteor Sinica,2012,70(2):302-310 (in Chinese)
周莉,兰明才,蔡荣辉等.2018.21世纪前期长江中下游流域极端降水预估及不确定性分析.气象学报,76(1):47-61.Zhou L,Lan M C,Cai R H,et al.2018.Projection and uncertainties of extreme precipitation over the Yangtze River valley in the early 21st century.Acta Meteor Sinica,76(1):47-61 (in Chinese)
周雅清,任国玉.2010.中国大陆1956—2008年极端气温事件变化特征分析.气候与环境研究,15(4):405-417.Zhou Y Q,Ren G Y.2010.Variation characteristics of extreme temperature indices in mainland China during 1956-2008.Clim Environ Res,15(4):405-417 (in Chinese)
Bao Y,Wen X Y.2017.Projection of China's near- and long-term climate in a new high-resolution daily downscaled dataset NEX-GDDP.J Meteor Res,31(1):236-249
Cao L J,Zhao P,Yan Z W,et al.2013.Instrumental temperature series in eastern and central China back to the nineteenth century.J Geophys Res Atmos,118(15):8197-8207
Chen H P,Sun J Q.2015.Assessing model performance of climate extremes in China:An intercomparison between CMIP5 and CMIP3.Clim Change,129(1-2):197-211
Chen H P,Sun J Q,Li H X.2017.Future changes in precipitation extremes over China using the NEX-GDDP high-resolution daily downscaled data-set.Atmos Ocean Sci Lett,10(6):403-410
Easterling D R,Alexander L V,Mokssit A,et al.2003.CCI/CLIVAR workshop to develop priority climate indices.Bull Amer Meteor Soc,84(10):1403-1407
Gao X J,Shi Y,Zhang D F,et al.2012.Climate change in China in the 21st century as simulated by a high resolution regional climate model.Chinese Sci Bull,57(10):1188-1195
Guo D L,Wang H J.2016.Comparison of a very-fine-resolution GCM with RCM dynamical downscaling in simulating climate in China.Adv Atmos Sci,33(5):559-570
Hawkins E,Sutton R.2009.The potential to narrow uncertainty in regional climate predictions.Bull Amer Meteor Soc,90(8):1095-1108
IPCC.2007.Climate Change 2007:The Physical Science Basis.Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.Cambridge:Cambridge University Press,996pp
IPCC.2013.Climate Change 2013:The Physical Science Basis.Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.Cambridge:Cambridge University Press,1535pp
Jiang D B,Tian Z P,Lang X M.2016.Reliability of climate models for China through the IPCC third to fifth assessment reports.Int J Climatol,36(3):1114-1133
Li W,Jiang Z H,Xu J J,et al.2016.Extreme precipitation indices over China in CMIP5 models.PartⅡ:Probabilistic projection.J Climate,29(24):8989-9004
Santer B D,Taylor K E,Gleckler P J,et al.2009.Incorporating model quality information in climate change detection and attribution studies.Proc Natl Acad Sci USA,106(35):14778-14783
Taylor K E,Stouffer R J,Meehl G A.2012.An overview of CMIP5 and the experiment design.Bull Amer Meteor Soc,93(4):485-498
Tebaldi C,Hayhoe K,Arblaster J,et al.2006.Going to the extremes:An intercomparison of model-simulated historical and future changes in extreme events.Clim Change,79(3-4):185-211
Thrasher B,Xiong J,Wang W L,et al.2013.Downscaled climate projections suitable for resource management.Eos,Trans Amer Geophys Union,94(37):321-323
Wang A H,Fu J J.2013.Changes in daily climate extremes of observed temperature and precipitation in China.Atmos Oceanic Sci Lett,2013,6(5):312-319
Xu Y,Gao X J,Shen Y,et al.2009.A daily temperature dataset over China and its application in validating a RCM simulation.Adv Atmos Sci,26(4):763-772